New alkaloids from Stemona tuberosa and structural revision of tuberostemonols P and R.

Three Stemona alkaloids named stemotuberines A-C (1-3) with unique C17N frameworks, presumably formed by elimination of the C-11-C-15 lactone ring of the stichoneurine skeleton, were isolated from the roots of Stemona tuberosa. Their structures were elucidated by spectroscopic analysis, X-ray diffraction, and computational methods. Compounds 2 and 3 showed inhibition (IC50 values of 37.1 and 23.2 µM, respectively) against LPS-induced nitric oxide production in RAW 264.7 cells. In addition, concern was expressed about the reported plant origin (S. sessilifolia) of the recently described alkaloids tuberostemonols O-R (4-7), which should be S. tuberosa. NMR calculations indicated structural misassignment of these compounds except for 6. Isolation of tuberostemonol P (5) from our material of S. tuberosa allowed for a close examination of the spectroscopic data leading to the revised structure 5a. Tuberostemonol R (7) was found to have identical 1H and 13C NMR data to the well-known alkaloid croomine, and therefore its structure including relative stereochemistry must be revised as 7a.

Clinical analysis, diagnosis, and treatment strategies for otogenic brain abscesses.

Objective: To explore the clinical features, diagnosis, and treatment strategies for otogenic brain abscesses (OBA). Methods: Clinical data from 10 patients with OBA were analyzed retrospectively. Clinical characteristics, diagnosis, and treatment experiences were summarized. Results: Two were first diagnosed in the Department of Otorhinolaryngology, 5 in Neurosurgery, and 3 in other departments. There were 6 cases of temporal lobe abscess and 4 cases of cerebellar abscesses. Five cases were accompanied by 1 or more intracranial complications. Headache is a common presentation in all cases. The main pathogenic bacteria were anaerobic bacteria. All patients had no previous ear or brain surgery history, and no history of traumatic brain injury, 7 received surgical treatment in the neurosurgery and/or otolaryngology department. Two patients died, the other 8 fully recovered and so were discharged. Conclusions: Diagnosis and treatment of OBA must involve multiple departments. Multidisciplinary consultation (MDT) is crucial to the success of the first OBA diagnosis. The diagnosis and treatment team develops personalized treatment plans by integrating MDT treatment opinions and combining the actual condition of patients, thereby making the diagnosis and treatment of OBA accurate and timely.

Unsupervised Domain-Adaptive Object Detection via Localization Regression Alignment.

Unsupervised domain-adaptive object detection uses labeled source domain data and unlabeled target domain data to alleviate the domain shift and reduce the dependence on the target domain data labels. For object detection, the features responsible for classification and localization are different. However, the existing methods basically only consider classification alignment, which is not conducive to cross-domain localization. To address this issue, in this article, we focus on the alignment of localization regression in domain-adaptive object detection and propose a novel localization regression alignment (LRA) method. The idea is that the domain-adaptive localization regression problem can be transformed into a general domain-adaptive classification problem first, and then adversarial learning is applied to the converted classification problem. Specifically, LRA first discretizes the continuous regression space, and the discrete regression intervals are treated as bins. Then, a novel binwise alignment (BA) strategy is proposed through adversarial learning. BA can further contribute to the overall cross-domain feature alignment for object detection. Extensive experiments are conducted on different detectors in various scenarios, and the state-of-the-art performance is achieved; these results demonstrate the effectiveness of our method. The code will be available at: https://github.com/zqpiao/LRA.

Mitochondrial genomic analyses provide new insights into the "missing" atp8 and adaptive evolution of Mytilidae.

BACKGROUND: Mytilidae, also known as marine mussels, are widely distributed in the oceans worldwide. Members of Mytilidae show a tremendous range of ecological adaptions, from the species distributed in freshwater to those that inhabit in deep-sea. Mitochondria play an important role in energy metabolism, which might contribute to the adaptation of Mytilidae to different environments. In addition, some bivalve species are thought to lack the mitochondrial protein-coding gene ATP synthase F0 subunit 8. Increasing studies indicated that the absence of atp8 may be caused by annotation difficulties for atp8 gene is characterized by highly divergent, variable length. RESULTS: In this study, the complete mitochondrial genomes of three marine mussels (Xenostrobus securis, Bathymodiolus puteoserpentis, Gigantidas vrijenhoeki) were newly assembled, with the lengths of 14,972 bp, 20,482, and 17,786 bp, respectively. We annotated atp8 in the sequences that we assembled and the sequences lacking atp8. The newly annotated atp8 sequences all have one predicted transmembrane domain, a similar hydropathy profile, as well as the C-terminal region with positively charged amino acids. Furthermore, we reconstructed the phylogenetic trees and performed positive selection analysis. The results showed that the deep-sea bathymodiolines experienced more relaxed evolutionary constraints. And signatures of positive selection were detected in nad4 of Limnoperna fortunei, which may contribute to the survival and/or thriving of this species in freshwater. CONCLUSIONS: Our analysis supported that atp8 may not be missing in the Mytilidae. And our results provided evidence that the mitochondrial genes may contribute to the adaptation of Mytilidae to different environments.

Experimental Study on Mechanical Properties of Rebar in Steel Half-Grouted Sleeve Connections with Construction Defects.

A prefabricated concrete structure is a building structure designed for sustainability and low comprehensive carbon emission. The grouted sleeve splice is a major connection method for prefabricated concrete structures. However, construction defects occur easily in the grouted sleeve splice connection at construction sites because of complex construction environments and the high connection accuracy. To determine the influence of rebar in steel half-grouted sleeve connections with construction defects, investigations were conducted using four different test groups (rebar offset, rebar bended, insufficient fluidity of grout, and control group). The load-displacement curve and load-stress curve were analyzed on 24 different specimens through uniaxial tension experiments. The experimental results showed that rebar fracture was the failure of specimens. The load-displacement curves consisted of elastic, yield, strength, and tight stages. The curves were similar to rebar under uniaxial tension, except for the rebar bended group. The axial stress and circumferential stress on the sleeve surface consistently followed a linear response before the specimen yield, whereas the axial stress and circumferential stress showed a rebound response after the specimen yielded. Different finite element models were established based on the different defects. Compared with the experimental results, the finite element analysis results coincided with those of the experimental results, and the errors were within 8% to evaluate the performance of steel half-grouted sleeve connections in construction.

Structural Revision of the Stemona Alkaloids Tuberostemonine O, Dehydrocroomines A and B, and Dehydrocroomine.

The structural revision of four Stemona alkaloids from Stemona tuberosa is reported. The misassignment of the tuberostemonine O structure (1) was recognized when a new alkaloid, tuberostemonine P, was isolated and unambiguously assigned structure 1 in this work. Reinvestigation of the spectroscopic data and NMR calculations led to the revised structure 1a for tuberostemonine O. The structural misassignment of dehydrocroomine A as 2 was corrected by reinterpreting the X-ray crystal structure, which was consistent with 2a. The structural reassignments of dehydrocroomine B (3 to 3a) and dehydrocroomine (4 to 4a) were confirmed by X-ray crystallography and NMR calculations, respectively.

Lycojapomines A-E: Lycopodium Alkaloids with Anti-Renal Fibrosis Potential from Lycopodium japonicum.

Five Lycopodium alkaloids featuring novel C17N2 (1 and 2), C29N3 (3 and 4), and C15N2 (5) skeletons were isolated from Lycopodium japonicum. Compound 1 is the first natural product containing a 3-aza[3.3.3]propellane motif. The structures of these compounds were elucidated by spectroscopic analysis, X-ray crystallography, and computational methods. Compounds 1 and 3-5 significantly inhibited TGF-ß1-induced fibronectin deposition in HK-2 cells at a nontoxic concentration of 20 µM.

Aquaculture Molecular Breeding Platform (AMBP): a comprehensive web server for genotype imputation and genetic analysis in aquaculture.

It is of vital importance to understand the population structure, dissect the genetic bases of performance traits, and make proper strategies for selection in breeding programs. However, there is no single webserver covering the specific needs in aquaculture. We present Aquaculture Molecular Breeding Platform (AMBP), the first web server for genetic data analysis in aquatic species of farming interest. AMBP integrates the haplotype reference panels of 18 aquaculture species, which greatly improves the accuracy of genotype imputation. It also supports multiple tools to infer genetic structures, dissect the genetic architecture of performance traits, estimate breeding values, and predict optimum contribution. All the tools are coherently linked in a web-interface for users to generate interpretable results and evaluate statistical appropriateness. The webserver supports standard VCF and PLINK (PED, MAP) files, and implements automated pipelines for format transformation and visualization to simplify the process of analysis. As a demonstration, we applied the webserver to Pacific white shrimp and Atlantic salmon datasets. In summary, AMBP constitutes comprehensive resources and analytical tools for exploring genetic data and guiding practical breeding programs. AMBP is available at http://mgb.qnlm.ac.

Evolution of major histocompatibility complex class I genes in the sable Martes zibellina (Carnivora, Mustelidae).

The molecules encoded by major histocompatibility complex (MHC) genes play an essential role in the adaptive immune response among vertebrates. We investigated the molecular evolution of MHC class I genes in the sable Martes zibellina. We isolated 26 MHC class I sequences, including 12 putatively functional sequences and 14 pseudogene sequences, from 24 individuals from two geographic areas of northeast China. The number of putatively functional sequences found in a single individual ranged from one to five, which might be at least 1-3 loci. We found that both balancing selection and recombination contribute to evolution of MHC class I genes in M. zibellina. In addition, we identified a candidate nonclassical MHC class I lineage in Carnivora, which may have preceded the divergence (about 52-57 Mya) of Caniformia and Feliformia. This may contribute to further understanding of the origin and evolution of nonclassical MHC class I genes. Our study provides important immune information of MHC for M. zibellina, as well as other carnivores.

The breeding and the performance of 'Liaoning 4' walnut cultivar.

Walnut is an important cultivated tree with high economic value and wide distribution across China. The objective was to breed a new walnut cultivar with high yield and desirable nut and tree qualities that could meet the demands of the walnut industry in China. 'Liaoning 4' walnut cultivar is lateral bearing which originated from a controlled cross between the Persian walnut (J. regia) selections 'Liaoning Chaoyang big and rough walnut' and '11001'. During long term assessment and research, 'Liaoning 4' walnut performed well and it was released as a new cultivar in China on 15 Oct. 2018. It has a high yield and excellent nut traits and has thus been widely planted in North China.

High-Performance Visual Tracking With Extreme Learning Machine Framework.

In real-time applications, a fast and robust visual tracker should generally have the following important properties: 1) feature representation of an object that is not only efficient but also has a good discriminative capability and 2) appearance modeling which can quickly adapt to the variations of foreground and backgrounds. However, most of the existing tracking algorithms cannot achieve satisfactory performance in both of the two aspects. To address this issue, in this paper, we advocate a novel and efficient visual tracker by exploiting the excellent feature learning and classification capabilities of an emerging learning technique, that is, extreme learning machine (ELM). The contributions of the proposed work are as follows: 1) motivated by the simplicity and learning ability of the ELM autoencoder (ELM-AE), an ELM-AE-based feature extraction model is presented, and this model can provide a compact and discriminative representation of the inputs efficiently and 2) due to the fast learning speed of an ELM classifier, an ELM-based appearance model is developed for feature classification, and is able to rapidly distinguish the object of interest from its surroundings. In addition, in order to cope with the visual changes of the target and its backgrounds, the online sequential ELM is used to incrementally update the appearance model. Plenty of experiments on challenging image sequences demonstrate the effectiveness and robustness of the proposed tracker.

Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis.

Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving, an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.

State-aware Anti-drift Object Tracking.

Correlation filter (CF) based trackers have aroused increasing attentions in visual tracking field due to the superior performance on several datasets while maintaining high running speed. For each frame, an ideal filter is trained in order to discriminate the target from its surrounding background. Considering that the target always undergoes external and internal interference during tracking procedure, the trained tracker should not only have the ability to judge the current state when failure occurs, but also to resist the model drift caused by challenging distractions. To this end, we present a State-aware Anti-drift Tracker (SAT) in this paper, which jointly model the discrimination and reliability information in filter learning. Specifically, global context patches are incorporated into filter training stage to better distinguish the target from backgrounds. Meanwhile, a color-based reliable mask is learned to encourage the filter to focus on more reliable regions suitable for tracking. We show that the proposed optimization problem could be efficiently solved using Alternative Direction Method of Multipliers and fully carried out in Fourier domain. Furthermore, a Kurtosis-based updating scheme is advocated to reveal the tracking condition as well as guarantee a high-confidence template updating. Extensive experiments are conducted on OTB-100 and UAV-20L datasets to compare the SAT tracker with other relevant state-of-the-art methods. Both quantitative and qualitative evaluations further demonstrate the effectiveness and robustness of the proposed work.

Development of microsatellite markers for the Siberian weasel Mustela sibirica.

The Siberian weasel (Mustela sibirica) is widely distributed in mainland Asia, but its introduction into Japan and subsequent expansion have affected the Japanese weasel (M. itatsi). To provide a useful tool for population genetic studies and control of M. sibirica, we developed 10 polymorphic microsatellite markers. Among 40 individuals of M. sibirica collected in Hubei Province, China, the number of alleles per locus varied from 2 to 19, with the observed heterozygosity ranging from 0.050 to 1.000 and the expected heterozygosity ranging from 0.049 to 0.920. None of the loci deviated from Hardy-Weinberg equilibrium. These markers will be useful in further studies investigating the population structure and natural history of M. sibirica, and may thus provide new insights for the efficient management of this species.

Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery.

Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX's output of each pixel is restrained by a nonlinear function and then imposed as a coefficient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods.

Edge-Aware Unidirectional Total Variation Model for Stripe Non-Uniformity Correction.

The problem of stripe non-uniformity in array-based infrared imaging systems has been the focus of many research studies. Among the proposed correction techniques, total variation models have been proven to significantly reduce the effect of this type of noise on the captured image. However, they also cause the loss of some image details and textures due to over-smoothing effect. In this paper, a correction scheme is proposed based on unidirectional variation model to exploit the direction characteristic of the stripe noise, in which an edge-aware weighting is incorporated to convey image structure retaining ability to the overall algorithm. Moreover, a statistical-based regularization is also introduced to further enhance correction performance around strong edges. The proposed approach is thoroughly scrutinized and compared to the state-of-the-art de-striping techniques using real stripe non-uniform images. Results demonstrate a significant improvement in edge preservation with better correction performance.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

Robust Object Tracking With Discrete Graph-Based Multiple Experts.

Variations of target appearances due to illumination changes, heavy occlusions, and target deformations are the major factors for tracking drift. In this paper, we show that the tracking drift can be effectively corrected by exploiting the relationship between the current tracker and its historical tracker snapshots. Here, a multi-expert framework is established by the current tracker and its historical trained tracker snapshots. The proposed scheme is formulated into a unified discrete graph optimization framework, whose nodes are modeled by the hypotheses of the multiple experts. Furthermore, an exact solution of the discrete graph exists giving the object state estimation at each time step. With the unary and binary compatibility graph scores defined properly, the proposed framework corrects the tracker drift via selecting the best expert hypothesis, which implicitly analyzes the recent performance of the multi-expert by only evaluating graph scores at the current frame. Three base trackers are integrated into the proposed framework to validate its effectiveness. We first integrate the online SVM on a budget algorithm into the framework with significant improvement. Then, the regression correlation filters with hand-crafted features and deep convolutional neural network features are introduced, respectively, to further boost the tracking performance. The proposed three trackers are extensively evaluated on three data sets: TB-50, TB-100, and VOT2015. The experimental results demonstrate the excellent performance of the proposed approaches against the state-of-the-art methods.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array.

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.